Unstructured Grid Visualization Cookbook

Overview

Unstructured Grids are a powerful way to represent Geoscience data. Unlike the more commonly used Structured Grids, Unstructured Grids are composed of flexible geomentries, which allows for a customizable discretization of the Earth with a variable resolution. This makes them incredibly useful for filling in irregularly shaped domains like Earth’s oceans, or for achieving high resolutions in localized regions. However, working and analyzing Unstructured Grids requires additional overhead, since the mesh is represented as a set of points, edges, and faces. In this notebook, we will cover the following:

MPAS Ocean Mesh Dataset
Working with Unstructured Grids using UXarray
Plotting with HoloViz
Plotting with Matplotlib

Prerequisites

This section was inspired by this template of the wonderful The Turing Way Jupyter Book.

Following your overview, tell your reader what concepts, packages, or other background information they’ll need before learning your material. Tie this explicitly with links to other pages here in Foundations or to relevant external resources. Remove this body text, then populate the Markdown table, denoted in this cell with | vertical brackets, below, and fill out the information following. In this table, lay out prerequisite concepts by explicitly linking to other Foundations material or external resources, or describe generally helpful concepts.

Label the importance of each concept explicitly as helpful/necessary.

Concepts	Importance	Notes
Intro to Cartopy	Necessary
Understanding of NetCDF	Helpful	Familiarity with metadata structure
Project management	Helpful

Time to learn: estimate in minutes. For a rough idea, use 5 mins per subsection, 10 if longer; add these up for a total. Safer to round up and overestimate.
System requirements:
- Populate with any system, version, or non-Python software requirements if necessary
- Otherwise use the concepts table above and the Imports section below to describe required packages as necessary
- If no extra requirements, remove the System requirements point altogether

Imports

# Organization and data handling
import numpy as np
import pandas as pd
import uxarray as ux
import xarray as xr
import time
import requests

# General Plotting
import cartopy.crs as ccrs
import cartopy.feature as cfeature

# Plotting with matplotlib
import matplotlib
import matplotlib.pyplot as plt
from matplotlib.collections import LineCollection

# Plotting with datashader
import holoviews as hv
import hvplot.pandas
import geoviews.feature as gf

import warnings
warnings.filterwarnings("ignore")

# Create blue color theme
cmap = matplotlib.colors.LinearSegmentedColormap.from_list("Color_Theme", plt.cm.Blues(np.linspace(0.2, 1, 30)))

Working with Unstructured Grids using UXarray

Loading Datasets

uxds_480 = ux.open_dataset(ds_file_480, ds_file_480)
uxds_480.uxgrid

<uxarray.Grid>
Original Grid Type: mpas
Grid Dimensions:
  * nMesh2_node: 3947
  * nMesh2_face: 1791
  * nMaxMesh2_face_nodes: 6
  * nMesh2_edge: 5754
Grid Coordinate Variables:
  * Mesh2_node_x: (3947,)
  * Mesh2_node_y: (3947,)
  * Mesh2_face_x: (1791,)
  * Mesh2_face_y: (1791,)
Grid Connectivity Variables:
  * Mesh2_face_nodes: (1791, 6)
  * Mesh2_edge_nodes: (5754, 2)
  * nNodes_per_face: (1791,)

uxds_120 = ux.open_dataset(ds_file_120, ds_file_120)
uxds_120.uxgrid

<uxarray.Grid>
Original Grid Type: mpas
Grid Dimensions:
  * nMesh2_node: 59329
  * nMesh2_face: 28571
  * nMaxMesh2_face_nodes: 6
  * nMesh2_edge: 87980
Grid Coordinate Variables:
  * Mesh2_node_x: (59329,)
  * Mesh2_node_y: (59329,)
  * Mesh2_face_x: (28571,)
  * Mesh2_face_y: (28571,)
Grid Connectivity Variables:
  * Mesh2_face_nodes: (28571, 6)
  * Mesh2_edge_nodes: (87980, 2)
  * nNodes_per_face: (28571,)

UGRID Conventions

Plotting with HoloViz

Conversion to `GeoDataFrame`

gdf_480_grid = uxds_480.uxgrid.to_geodataframe()
gdf_120_grid = uxds_120.uxgrid.to_geodataframe()

Nodes

hv.extension("matplotlib")

plot_kwargs = {"size": 5.0, "xlabel":"Longitude", "ylabel": "Latitude", "xlim": (-110, -50), "ylim": (0, 40),
               "coastline": True, "width": 800}

 hv.Layout(gdf_480_grid.hvplot.points(**plot_kwargs) + gdf_120_grid.hvplot.points(**plot_kwargs)).opts(fig_size=300).cols(1)

hv.extension("bokeh")

plot_kwargs = {"s": 4.0, "xlabel":"Longitude", "ylabel": "Latitude", "xlim": (-110, -50), "ylim": (0, 40),
               "coastline": True}

hv.Layout(gdf_480_grid.hvplot.points(**plot_kwargs) + gdf_120_grid.hvplot.points(**plot_kwargs)).cols(1)

Edges

hv.extension("matplotlib")

plot_kwargs = {"linewidth": 0.5, "xlabel":"Longitude", "ylabel": "Latitude", "xlim": (-110, -50), "ylim": (0, 40),
               "coastline": True, "width": 1600}

hv.Layout(gdf_480_grid.hvplot.paths(**plot_kwargs) + gdf_120_grid.hvplot.paths(**plot_kwargs)).opts(fig_size=300).cols(1)

hv.extension("bokeh")

plot_kwargs = {"line_width": 0.5, "xlabel":"Longitude", "ylabel": "Latitude", "xlim": (-110, -50), "ylim": (0, 40),
               "coastline": True}

hv.Layout(gdf_480_grid.hvplot.paths(**plot_kwargs) + gdf_120_grid.hvplot.paths(**plot_kwargs)).cols(1)

Conversion to `GeoDataFrame` (2, add better title)

gdf_480_grid_data = uxds_480['bottomDepth'].to_geodataframe()
gdf_120_grid_data = uxds_120['bottomDepth'].to_geodataframe()

Polygons

hv.extension("matplotlib")

plot_kwargs = {"c": "bottomDepth", "cmap": cmap} #TODO 

# TODO: vector, not working properly, see https://github.com/holoviz/hvplot/issues/1099

# TODO: raster
hv.Layout(gdf_480_grid_data.hvplot.polygons(**plot_kwargs, rasterize=True) + 
          gdf_120_grid_data.hvplot.polygons(**plot_kwargs, rasterize=True)).opts(fig_size=100).cols(1)

hv.extension("bokeh")

plot_kwargs = {"c": "bottomDepth",  "cmap": cmap, "line_width": 0.1} #TODO 

# TODO: vector
hv.Layout(gdf_480_grid_data.hvplot.polygons(**plot_kwargs, rasterize=False) + 
          gdf_120_grid_data.hvplot.polygons(**plot_kwargs, rasterize=False)).cols(1)

# TODO: raster
hv.Layout(gdf_480_grid_data.hvplot.polygons(**plot_kwargs, rasterize=True) + 
          gdf_120_grid_data.hvplot.polygons(**plot_kwargs, rasterize=True)).cols(1)

Plotting with Matplotlib

Conversion to `PolyCollection`

Comparison between Plotting Methods

Timing

Memory Usage

Summary

Add one final --- marking the end of your body of content, and then conclude with a brief single paragraph summarizing at a high level the key pieces that were learned and how they tied to your objectives. Look to reiterate what the most important takeaways were.

What’s next?

Let Jupyter book tie this to the next (sequential) piece of content that people could move on to down below and in the sidebar. However, if this page uniquely enables your reader to tackle other nonsequential concepts throughout this book, or even external content, link to it here!

Resources and references

Finally, be rigorous in your citations and references as necessary. Give credit where credit is due. Also, feel free to link to relevant external material, further reading, documentation, etc. Then you’re done! Give yourself a quick review, a high five, and send us a pull request. A few final notes:

Kernel > Restart Kernel and Run All Cells... to confirm that your notebook will cleanly run from start to finish
Kernel > Restart Kernel and Clear All Outputs... before committing your notebook, our machines will do the heavy lifting
Take credit! Provide author contact information if you’d like; if so, consider adding information here at the bottom of your notebook
Give credit! Attribute appropriate authorship for referenced code, information, images, etc.
Only include what you’re legally allowed: no copyright infringement or plagiarism

Thank you for your contribution!